\[ x \beta = \beta_0 + \beta_1X_i + \cdots + \beta_nX_n \\ \] \[ prob = {\frac{exp(x\beta)}{1 + exp (x\beta)}} \]
\[ prob = \frac{exp( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n )} {1 + exp ( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n)} \]
\[ prob = \frac {1} {1 + e^{ -( \beta_0 + \beta_1X_i + \cdots + \beta_nX_n) }} \]
| Name | Piped data |
| Number of rows | 20 |
| Number of columns | 2 |
| _______________________ | |
| Column type frequency: | |
| numeric | 2 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| 학습시간 | 0 | 1 | 2.79 | 1.51 | 0.5 | 1.69 | 2.62 | 4.06 | 5.5 | ▇▇▆▅▅ |
| 입학여부 | 0 | 1 | 0.50 | 0.51 | 0.0 | 0.00 | 0.50 | 1.00 | 1.0 | ▇▁▁▁▇ |
Call: glm(formula = 입학여부 ~ 학습시간, family = "binomial",
data = lr_tbl)
Coefficients:
(Intercept) 학습시간
-4.078 1.505
Degrees of Freedom: 19 Total (i.e. Null); 18 Residual
Null Deviance: 27.73
Residual Deviance: 16.06 AIC: 20.06
데이터 과학자 이광춘 저작
kwangchun.lee.7@gmail.com